390 papers
Biophysics sits at the fascinating intersection where the laws of physics meet the complexity of living systems. This field uses tools like light, electricity, and mechanical forces to decode how cells move, how proteins fold, and how our senses translate the world around us. Rather than just observing biology, biophysicists measure and model life to understand the fundamental machinery that powers every organism.
On Gist.Science, we make these discoveries accessible by curating the latest preprints directly from bioRxiv. Our team processes every new submission in this category, providing both clear, plain-language overviews and detailed technical summaries so readers of all backgrounds can grasp the cutting-edge science. Below are the most recent biophysics papers from bioRxiv, ready for you to explore.
This study utilizes in-cell cryo-electron tomography to demonstrate that type I kinase inhibitors (MLi-2) promote extensive microtubule-associated LRRK2 filament formation and bundling, enabling the construction of a full-length structural model, whereas type II inhibitors (GZD-824) prevent such assemblies.
This paper establishes a conceptual framework for nonequilibrium calorimetry to study biological systems, demonstrating through biophysical models of ciliary and molecular motor motion that biological activity can lead to intriguing dependencies in heat capacity, including negative values.
By integrating cryo-EM structures with computational analyses, this study elucidates the molecular mechanisms of gating and isoform-specific inhibition in renal CLC-K chloride channels, revealing how a dynamic extracellular loop and subtle electrostatic differences in the binding pocket enable the selective targeting of CLC-Ka over CLC-Kb for treating hyponatremia.
This study demonstrates that in time-varying spatially structured environments, slow but nonzero migration can synergize with environmental fluctuations to accelerate the extinction and eradication of cooperative antimicrobial resistance, challenging the conventional view that dispersal generally promotes strain coexistence.
This study demonstrates that the loss modulus of viscoelastic extracellular matrices significantly alters A549 adenocarcinoma cell migration speed and focal adhesion dynamics compared to elastic substrates, highlighting the critical role of time-dependent matrix mechanics in regulating epithelial mechanobiology.
This study introduces a DNA-PAINT-based single-molecule super-resolution workflow to quantitatively map nanoscale EGFR-Grb2 assemblies, revealing that EGF stimulation drives EGFR oligomerization and Grb2 accumulation while reducing overall EGFR density at the plasma membrane.
This study demonstrates that alternative splicing of exon 35 in the Piezo2 ion channel confers high sensitivity to membrane tension, enabling distinct physiological variants to sense mechanical forces with unique dynamic ranges that support diverse somatosensory and interoceptive functions.
This paper presents a scalable, automated pipeline that integrates protein design with high-throughput NMR spectroscopy to experimentally characterize the structure and dynamics of hundreds of de novo designed proteins, revealing conformational heterogeneity not captured by current computational models and establishing a foundation for data-driven statistical structural biology.
This study combines single-molecule force spectroscopy and molecular dynamics simulations to reveal that the double-knotted TrmD-Tm1570 protein exhibits unique mechanical unfolding pathways and higher stability than its single-knotted counterparts, suggesting that native contacts alone are insufficient for its complete folding and that chaperone assistance may be required.
This paper utilizes automated model discovery on over a hundred compression tests to reveal that the nonlinear mechanics of tofu are governed by a universal, highly non-linear relationship between water content and feature networks, establishing it as a quantitative benchmark for hydrated soft solids and demonstrating the power of physics-informed machine learning in uncovering constitutive laws.